A field guide to choosing data stores: data shape, access pattern and consistency pick the class, operational defaults decide whether it stays safe. Two failure stories from the field.
Choose event delivery by semantics, not product names. Compare SSE, PostgreSQL, RabbitMQ, Kafka, Pulsar and AutoMQ by durability, replay and failure behavior.
A field guide to performance and cost engineering: claim-checking with the scaling and queueing laws, measurement discipline, where GPU and CPU budgets leak, and how to turn a benchmark into a hardware decision.
A field guide to running AI in production: the supply chain as a dependency, quiet failure as the default mode, the verification gate as the method, and cost per correctly solved task as the metric.
A field guide to Ceph in production: which surface serves which workload, what failure domains cost in replicas, how the upgrade lifecycle really rolls back, and what key rotation does not do.
Ceph tenants and accounts solve different problems. Separate the Tentacle upgrade from account adoption, then test ownership, IAM and notifications.
A DR document states minutes and megabytes. Replication architecture delivers a convergence time, a single-copy window and a failed tail. This guide maps what actually decides your RTO and RPO.
RTO and RPO are measurable recovery objectives, not labels on an architecture diagram. Build a failure matrix and test the complete recovery path.
A production guide to running object storage at scale: why object count is a capacity axis, how prefix design and the background scanner set your real SLAs, and how to choose engines, media and release channels.
Second in my benchmark best practices series. Little’s law checks that throughput, latency and concurrency describe the same system, exposes the load generator’s ceiling, and turns a latency budget into a sizing requirement.